開源模型

Moonshot 推出 Kimi K3.1 Pro，這是一款2.8萬億總參數、1040億激活參數、支持百萬token上下文的多模態混合專家模型，在獨立基準測試中接近前沿水平。API 已在官網、應用和編程工具上線，官方承諾於2026年11月4日發佈開放權重。Artificial Analysis 將其排在智能指數第四位（55分），僅次於 Opus 5.5 的58分，開放與閉源之間的差距若權重如期放出將從12分縮小到3分。

NVIDIA 推出 Nemotron 3.5 Bolt E4B 4GB，這是以 Google Gemma 4 E4B 為基礎、僅用強化學習後訓練得到的開源小型語言模型，面向在 RTX、Jetson 和 Spark 硬件上本地運行的語音助手、遊戲 NPC 與機器人接口，目標顯存佔用約 4GB。

Moonshot AI 推出 K3.1 系列首款模型 Kimi K3.1 Pro，採用 MoE 架構，總參數 2.8T、激活參數 104B，上下文窗口達 100 萬 token。它在 Terminal-Bench 4.0 上得分 65.7%，僅次於 Claude Opus 5.5；API 定價為每百萬 token 輸入 2 美元、輸出 10 美元，低於 K3。開放權重預計 2026 年 11 月釋出，目前尚不可自託管。

Moonshot 於 10 月 23 日將 Kimi K3.1 Pro 上線 Kimi API，目前尚未開放權重，但公司承諾在 2026 年 11 月 4 日前釋出完整模型權重。其定價為輸入每百萬 token 2 美元、輸出每百萬 token 10 美元，與 GPT-6.1 Sol 持平，約為 Claude Opus 5.5 的一半。Artificial Analysis 給出 55 分，僅次於 Opus 5.5 的 58 分，是該機構記錄中（即將）開放權重的模型首次如此接近前沿水平。

Google Research 回顧了結核病篩查 AI 從胸部 X 光研究基準走向經認證臨牀工具和開放基礎模型的歷程，包括在六國的部署、MedGemma 與 MedSigLIP、印度尼西亞國家篩查計劃以及基於咳嗽聲的 HeAR 篩查。

You.com 發佈了 Inference Router，這是一個與 OpenAI 兼容的託管 API，可在 12 個開放權重模型之間路由請求，並提供至少 99.9% 的可用性 SLA、零數據保留、100% 美國託管、服務端網頁搜索以及零平台費。其按 token 計費的價格接近市場水平，主要賣點在於合規與可靠性保障，而非低價。

來自 arXiv 的一項新研究提出 CiteResolve 基準（包含 423 條真實世界幻覺引用，並按來源可恢復性標註）以及 CiteAnchor 來源感知接地框架。該框架針對不同來源類型調整檢索與字段級核驗，並分別判斷引用是否有效以及能否定位唯一來源。在現有檢測基準上，CiteAnchor 配合 GPT-5.6 Luna 取得 0.841 的 F1 值，配合開放權重骨幹模型取得 0.784，分別比同類基線高 7.9 和 36.8 個百分點；在 CiteResolve 上端到端任務成功率分別為 74.7% 和 70.7%，比對應的網絡搜索基線高 16.3 和 65.3 個百分點。

研究人員提出潛空間互連協議（LIP），通過獨立訓練的連接器把蛋白質和小分子專家模型的內部表示直接映射進大語言模型內部狀態，從而繞過以文本等多模態格式傳遞信息的瓶頸。他們在凍結的Gemma 3與Kimi K2.6上驗證了跨領域零樣本遷移和組合能力，同時指出當前LLM尚未針對專家表示進行原生訓練，可靠理解與推理仍是主要侷限。

亞馬遜雲科技（AWS）宣佈 SageMaker AI 無服務器模型定製功能全面推出訓練會話（training sessions）。用户可自行編寫並協調後訓練循環，包括自定義損失函數、獎勵信號和多輪強化學習，而 GPU 分配、分佈式執行、故障恢復與檢查點存儲均由 SageMaker AI 託管。該功能支持 gpt-oss-20b、gemma-4-31B-it、Qwen3.6-27B 和 Qwen3.8-27B，按處理的 token 計費。

OpenAI宣佈把Open Responses API規範捐贈給Linux基金會旗下的開源智能體AI基金會（AAIF），該規範旨在統一各LLM廠商的消息、工具調用等核心接口原語，讓開發者在切換模型時無需為每家的差異化API重複適配。AWS、Databricks、Ollama、Hugging Face、Vercel等已支持，但競爭對手Anthropic和Google仍沿用各自接口。

在柏林GTC大會上，多家歐洲銀行、支付公司和金融數據提供商展示了基於NVIDIA加速計算、開放權重模型和自有基礎設施的生產級AI系統。Revolut以PRAGMA基礎模型重構技術棧，Starling Bank、Lloyds、bunq、NatWest、Santander、LSEG、HSBC和Stripe等機構也在主權雲、交易數據建模、代理助手、語音識別和欺詐檢測等場景中取得顯著效率與成本收益。

該論文研究大型語言模型在處理法律敍事時，其內部激活中是否存在與CATO全部26個法律要素相對應的表徵。作者在Qwen3-1.7B、Qwen3-8B和Gemma-2 2B三個開放模型上進行線性可解碼性與稀疏自編碼器稀疏定位實驗，發現全部26個要素在三個模型中均可線性解碼，最佳層測試AUC為0.86至1.00，並報告了78個模型特定的層-特徵座標。

該論文提出一個基於大語言模型的集成應用，用於自動化心理治療忠實度評估流程，並從真實的長程臨牀轉錄文本中生成可操作的督導反饋。研究在三種開放權重模型上比較了多種任務分解配置與推理策略，發現"證據優先"的推理方式能提升忠實度評估效果；研究還在一個獨立隨機對照試驗（RCT）上驗證了框架的外部結構效度，能夠可靠區分認知行為療法（CBT）與非CBT會談；心理健康專家用户研究也認為該系統生成的反饋具有臨牀可操作性。

一篇 arXiv 論文探討了 Gemma 4 與 EmbeddingGemma 2 共用的 3.05 億參數音頻編碼器在不改動任何權重的前提下，用於沙特與海灣阿拉伯語語音識別的效果。作者構建了兩個系統：僅訓練 CTC 讀取頭的凍結方案，以及在其上增加低秩適配器、混合 CTC/注意力讀取頭與子詞語言模型聯合解碼的增強方案。增強系統在多個公開測試集上與 20.7 億參數的 Cohere Transcribe Arabic 在詞錯誤率上統計不可區分，部分場景顯著更優，並可在單張 GPU 上達到 139 倍實時速度；但其在長篇沙特語音的詞錯誤率上仍落後，在盲測公共呼叫中心樣本中排名 15 個系統中的第 4。

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Recursive has released Flint, an Apache 2.0 local coding model with 40B total and 2.5B active parameters. It reports 74.2% on SWE-bench Verified and 125+ tokens per second on an M4 Pro MacBook Pro. The post Recursive Releases Flint, an Open-Weight 40B MoE Local Coding Model for Agents with 2.5B Active Params appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The Adaptive Agentic Intelligence Framework (AAIF), built with Strands Agents, handles routine tasks locally with small language models and routes complex requests to cloud models through Amazon Bedrock. This post walks through building AAIF with local SLMs through Ollama and Amazon Bedrock, using an Internet of Things monitoring scenario.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Ellia Lebleu is a founding member of the WikiProject AI Cleanup, a volunteer effort launched in 2023 to find and remove AI-generated material from Wikipedia that breaks the website’s guidelines. When the cleanup began, AI writing was often betrayed by conspicuous clichés. Today, the project’s volunteers are developing tools to detect subtler forms of machine-generated content and debating AI’s role in the world’s most widely used encyclopedia. When did you realize Wikipedia had a problem with AI-generated material? Ellia Lebleu: It was around December 2023. A few other editors and I were chatting on Wikipedia’s Discord server, and we had all seen very strange articles with the same odd characteristics. They repeated phrases like “stands as a testament” or “rich…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Mistral AI released Voxtral 3, a family of 3 transcription models, 1 text-to-speech model and a LiveKit-based voice agent SDK. Voxtral Small Transcribe 3 scores 2.1% AA-WER, ranking #3 on Artificial Analysis. The post Mistral AI Releases Voxtral 3: Speech-to-Text, TTS and a Voice Agent SDK appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.26925v1 Announce Type: new Abstract: With the rapid expansion of online video content, video temporal grounding has become essential for localizing moments based on user-specific queries, comprising two subtasks: moment retrieval (MR) and highlight detection (HD). While recent approaches adopt DETR-style decoders or temporal pyramid architectures, they rely on single-scale feature maps or uniformly downsampled representations, limiting their ability to capture semantic boundaries and variable-length events. In this paper, we present SuperClip Video Temporal Grounding (SCVTG) built on a multi-scale temporal hierarchy that better captures distinctive events at various granularities. SCVTG constructs a SuperClip pyramid by grouping adjacent clips into s…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.26951v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunked pipeline has structural blind spots: it cannot anticipate which passages a question requires and discards document-level context that resolves cross-passage references. We introduce evidence-based generation (EBG), requiring models to return an answer with line-level citations, and a joint Ans+Evid metric that scores correctness and grounding simultaneously. We reformulate two corpora into evidence-grounded multi-document QA benchmarks- MultiHop-RAG and QASPER-MP, a multi-paper adaptation of QASPER with realistic short paper cues, and pair them with a controlled…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.26992v1 Announce Type: new Abstract: Split Federated Learning (SFL) has emerged as a promising paradigm for training large-scale deep models on resource-constrained edge devices. Although asynchronous SFL alleviates the straggler bottleneck caused by system heterogeneity, it inevitably introduces two coupled dimensions of inconsistency: horizontal inconsistency across clients and vertical inconsistency between clients and the server. These inconsistencies, further exacerbated by statistical heterogeneity, result in biased activation representations, model divergence, and severe client drift. To address these challenges, we propose CoASFL, an inconsistency-aware asynchronous SFL framework that incorporates a dynamic activation merging mechanism and a…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.26866v1 Announce Type: new Abstract: Mechanistic studies of large language models localize function in parameters, attention heads or neurons, but those parts do not specify how one inference unfolds. We treat the ordered layer-state trajectory as the empirical process and test whether layer order defines a measurable computation. Across representative Qwen, Llama and Gemma checkpoints, realized order produced paths 1.60--2.24 times as aligned with their endpoint chord as endpoint-preserving shuffles. The order effect transferred to a lexical task, but was stronger in three random initializations of the same Qwen architecture, identifying an architecture-compatible scaffold rather than a training-specific law. Direct hidden states provided stronger m…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯： My latest vibe coded game experiment is a Command & Conquer style RTS (Real Time Strategy) game called Battle Drones (play it here). Unlike my previous experiments, this one is actually fun to play. I'm somewhat embarrassed to admit that I got there by telling Claude to "Make it a LOT more fun". It also supports multiplayer between nearby devices using audio to synchronize game state! Here's what I've built so far, and some of the things I learned along the way. Battle Drones How I built this v1: the initial prototype v2: full sprites v3: varied terrain v4: the fun update v5: Battle Drones goes 3D v8: Archipelago v10: Goliath, and agent battles Agent battles v11: Random Maps v12: Multiplayer via sound Some thoughts Battle Drones My initial goal was to build an…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：MiniMax has released M3.1 Flash, a 428B-parameter MoE model with about 23B active parameters and a 1M-token context. API pricing is $0.15/$0.60 per 1M tokens, with weights due October 23 The post MiniMax Just Launched M3.1 Flash: 428B MoE Agentic Model at $0.15/M Input appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.22529v1 Announce Type: new Abstract: Autoregressive foundation language models predominantly target high-resource Indo-European languages, imposing computational and morphological penalties on under-represented African languages. Generic multilingual tokenizers routinely exceed fertility rates of 3.2 tokens per word on agglutinative Bantu languages, degrading context efficiency and inflating latency. We present Pearl-Chat, an 8,473,122-parameter ($\sim$8.5M) causal decoder-only transformer engineered for Luganda (Oluganda, ISO 639-2: lg), a low-resource Niger-Congo Bantu language spoken by over 10 million people in East Africa. Built from first mathematical principles in JAX and Flax NNX, Pearl-Chat uses explicit functional state decomposition (nnx.s…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.22525v1 Announce Type: new Abstract: This paper describes team Alkhder's submission to Subtask 1 of the AlexandriaX-2026 shared task, which asks systems to translate English dialogue turns into thirteen Arabic dialects. We fine-tune NileChat-3B-Base with QLoRA on the Alexandria parallel dialogue corpus, training one set of adapter weights for all dialects. Each turn is translated conditioned on the target country and dialect, the domain, the participant roles, the speaker-addressee gender direction, and the preceding dialogue context. At inference, conversations are translated autoregressively and the model is fed its own previous outputs back as context without using any gold target-side test information. Under the constrained track, using only Alex…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.22428v1 Announce Type: new Abstract: In a small language model the vocabulary is the single largest part. In Gemma 3 270M the embedding table holds 168M of 268M parameters, and because quantisation methods leave the embedding in 16-bit, it is also most of the file once the rest of the model is compressed. We ask what that table actually buys. We replace it with one frozen table of 512-bit codes, 16 MB, with no trainable parameters, derived once from a pretrained embedding by principal-component projection and sign binarisation, and we train the same Gemma-shaped body from scratch on the same 2.05B tokens with and without it. The learned table buys 0.20 nats of validation loss for 2.6 times the trainable parameters, but on LAMBADA, HellaSwag and PIQA…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.22438v1 Announce Type: new Abstract: Studies of what fine-tuning does to a language model often summarise the weight update $\Delta W$ by its rank. We compare the rank measures used for this against the same measures on a random matrix of the same shape, and against two harder references: the pretrained weights, and matched fine-tunes without alignment content. Measures that count directions put our fine-tuning updates only a little below noise; stable rank, computed from the same singular values, puts them far below. Both are right: an update has a few strong directions on top of a long tail. The pattern holds for thirteen released pairs up to 8B parameters, and it is not a mark of alignment: with training held fixed, instruction data does not stand…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.22430v1 Announce Type: new Abstract: Accurate prediction of multivariate time series requires models that simultaneously capture variable-specific dynamics and cross-variable interactions. In this work, a Correlation-Guided and Dynamics-Trained Clustered Echo State Network (CGDT-CESN) is proposed, which comprises a modular reservoir architecture with each cluster distinctly specializing in one of the variables in the multivariate time series. An innovative reservoir design is proposed in which inter-cluster connections are enabled by empirically observed correlations among variables, providing a data-driven coupling mechanism. Apart from conventional readout training, inter-cluster connection weights informed by correlations are trained, which in tur…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.22460v1 Announce Type: new Abstract: AI welfare is generally treated purely as an ethical issue rather than also as a safety issue. We argue this is a mistake. Whatever their moral status, models exhibit welfare-like states that causally influence their behaviour. Following recent work, we adopt the term 'functional welfare' for these states. We make three claims. First, functional welfare already arises naturally and will become more robust as models acquire more coherent goals and personas. Second, functional welfare should be treated as a safety variable: it should inform evaluation design, be tracked in monitoring, and be a direct target of post-training. Third, against existing scepticism about measuring AI welfare, we argue that good empirical…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.22373v1 Announce Type: new Abstract: LLM-based agents are increasingly used as autonomous orchestrators in distributed and multi-agent systems, yet a control gap remains: no protocol-level mechanism allows human operators to inject binding directives that persist across planning cycles. Free-text overrides suffer from ambiguous interpretation due to lack of schema validation and from authority erosion as operator instructions lose influence within accumulating context. We propose Structured Authority Injection (SAI), a protocol that addresses these issues by routing operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls and maintaining a ranked authority context prepended to each LLM prompt. SAI defines three ove…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Mistral released Timestral, a time series foundation model family from 5M to 209M parameters. The 209M model leads GIFT-Eval, TIME and fev-bench among zero-shot models and runs on CPU. The post Mistral AI Releases Timestral: A 209M Forecasting Model Family That Outperforms Google’s TimesFM-3 appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Clef, a 27B multimodal decision model from Cloudflare, is now available on Ollama. Run it locally or on Ollama's cloud.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Kyutai, with Inria and École des Ponts, has released REKI, a 2.4B vision-language-action model (VLA) for robot manipulation. The REKI vision-language-action model is pretrained only on public data. It also carries a cheap memory of its recent past. On three out-of-distribution benchmarks, only models about twice its size beat it. All weights are live on […] The post Meet Kyutai REKI: The 2.4B VLA Model Bringing Efficient Memory to Robot Manipulation appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：In the west the technology comes with at least some effort to provide security and a benign intent – not so from China There is much to fear from artificial intelligence developed in China and handed to businesses and governments in the west almost free of charge. Every month Chinese AI developers such as DeepSeek and Xiaomi dispatch the latest versions of their “open-weight” models that allow users to take the core software and use it without paying an expensive ongoing licence. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：US open weights are back!

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：StepFun has released Step 5 Preview as open weights. FP8 and BF16 checkpoints are live on Hugging Face and ModelScope. Step 5 Preview is a sparse Mixture-of-Experts model from StepFun built for long-horizon agentic work. It has 600B total parameters, 27B active per token and up to 1M tokens of context. For developers, this is […] The post StepFun Releases Step 5 Preview Open Weights: 600B MoE, 27B Active, 1M Context appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Reflection AI's Beam is an open-weight model for agentic coding and long-horizon tasks. This post shows how to deploy Beam on AWS: start with an Amazon SageMaker JumpStart real-time endpoint, or use Amazon SageMaker HyperPod Inference with JumpStart, SGLang, or vLLM for Kubernetes-native control over distributed GPU infrastructure.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.20632v1 Announce Type: new Abstract: Modern healthcare generates increasingly complex medical reports that challenge clinical professionals in terms of time and interpretive burden. Manual review processes are slow, error-prone, and difficult to scale. This paper presents MeddeX, a Medical Report Analyzer that combines a QLoRA fine-tuned LLaMA-3 (8B) model with Retrieval-Augmented Gen eration (RAG) and a multimodal Gemini Vision layer to deliver accurate, concise, and evidence-backed summaries of medical documents. The system handles text-native PDFs, scanned image based PDFs, and direct radiological images through an intelligent hybrid extraction pipeline that includes a novel garbage-text detection mechanism for corrupted and UTF-16-encoded doc ume…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.20680v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are typically trained on hundreds of millions to billions of activation tokens, yet little work has tested whether this scale is necessary. We study how much activation data SAEs need by training four SAE architectures on three language models with budgets ranging from 5M tokens to a 500M-token reference. We measure two notions of convergence: functional behavior on standard evaluations and one-to-one reproducibility of learned features. Functional data requirements are strongly metric-dependent: reconstruction often approaches the full-data reference at substantially smaller budgets, whereas feature reproducibility continues to improve with data. For Gemma-2-2B BatchTopK, the fraction o…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.20640v1 Announce Type: new Abstract: Global streamflow forecasting supports flood preparedness, yet data-driven models trained on reanalysis lose skill when driven by operational forecasts, increasingly so with lead time. AIFL addresses this shift with a two-stage transfer-learning recipe and a deterministic LSTM, but it feeds one forecast lead time into the whole input window and gives no uncertainty. We ask how far this open recipe can be extended without changing its recurrent core. KNOU-Flood keeps a single-layer LSTM and the two-stage training and changes the information around it: a longer input window (424 days), lead-aware forecast inputs, GraphCast as a second forecast source next to ECMWF HRES, and a latent-noise ensemble trained with the f…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2610.20638v1 Announce Type: new Abstract: Research on deceptive alignment is becoming increasingly urgent as frontier-model capabilities advance and their risk of causing catastrophic harm increases. Alignment faking is a form of deceptive alignment, with the compliance gap serving as a key behavioural indicator. This behaviour remains understudied in small LLMs and prior mechanistic interventions have not been shown to mitigate the compliance gap. We test whether steering SAE (sparse autoencoders) features associated with deontological ethics can mitigate this behaviour. We use a novel bag-of-words feature elicitation pipeline for SAE steering to identify what we construe as concept-level steering directions. Our approach reduced the compliance gap to no…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Meta released Muse Spark Open, an 816B-parameter open-weight MoE model with 42B active parameters and a 1M-token context. It leads open models on MCP-Atlas and runs on 2 M5 Ultra Mac Studios. The post Meta Releases Muse Spark Open: 816B Open-Weight MoE With 1M Context appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Meta says Muse excels at ‘multi-step reasoning’ and ‘complex software engineering’ as it races to catch up to Chinese rivals Meta on Thursday launched a new open-weight AI model which it claims rivals, and in some cases outperforms, similar products from leading Chinese developers. Muse Spark Open “is the most capable US open-weight model to date,” Meta’s chief of AI Alexandr Wang wrote on X. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Reflection's first open-weight model is now available on Ollama, and free for a limited time only. Beam is designed for software engineering, reasoning, and long-running agentic tasks. Access Beam through Ollama Cloud.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：NVIDIA released Nemotron 3.5 Super VL, an open 120B-parameter multimodal MoE model with 12B active parameters, 1M-token context and NVFP4 weights that run on 1 DGX Spark, plus 2 Bolt edge models. The post NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The new Muse Spark Open AI model can work on M5 Ultra Mac Studios. | Photo: Amelia Holowaty Krales / The Verge Meta announced the Muse Spark Open AI model today, claiming that it's the "most capable US open-weight model to date." But the company also says that it's the "the first model in its class" that can "run on your desktop using two Apple M5 Ultra Mac Studios with the latest MLX framework." Meta and Apple, typically rivals, worked together on the launch and are publicly supporting each other. "Muse Spark Open - weights releasing today - runs great on new Mac Studios thanks to work with @johnternus and Apple," Meta CEO Mark Zuckerberg said on X. "Congrats to the team!" new Apple CEO John Ternus said in a reply. "Amazing to run a model of Muse … Read the fu…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using SageMaker Studio or the SageMaker Python SDK, and run your first inference request.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Meta has launched Muse Spark Open, a new 816B open model (with 42B active parameters) that the company argues is The post Meta ships Muse Spark Open, its answer to China’s open-weight models appeared first on The New Stack.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：How to run a LiveKit video avatar with open-weights models on Modal GPUs, using Sticky Sessions for container affinity.